An automated industrial anti-corrosion coating intelligent production scheduling system

Abstract

The present invention discloses an automated industrial anti-corrosion coating intelligent production scheduling system, which relates to the field of industrial production technology. In order to solve the problem that when facing orders for industrial anti-corrosion coatings of multiple varieties and small batches, the system's flexibility and response speed are insufficient, and problems in the production process cannot be discovered and solved in time, resulting in delayed production progress and failure to deliver products on time; the present invention can quickly receive and parse order information through an order parsing unit, generate a production scheduling plan based on real-time data and preset rules, and accurately send production task instructions to each production equipment through a task conversion module and a task distribution module, while tracking the task execution status in real time, discovering and solving problems in the production process in time, and providing an intuitive production status display, supporting users to quickly understand the production progress and make adjustments, ensuring that the production progress is carried out as planned, improving the ability to deliver products on time, and significantly improving production efficiency and customer satisfaction.

Description

Technical Field

[0001] The invention relates to the technical field of industrial production, and in particular to an automated intelligent production scheduling system for industrial anti-corrosion coatings. Background Art

[0002] The traditional coating production scheduling method mainly relies on manual experience, and there are problems such as low production efficiency, waste of resources, and delayed delivery. For example, the patent application with publication number: CN104504484A discloses a coating production scheduling management system, which includes a production data storage server, a production planning and scheduling system, and multiple automated production lines. The production planning and scheduling system is connected to the production data storage server and multiple automated production lines, wherein the production planning and scheduling system includes an authority setting module, a data acquisition module, a production scheduling module, an analysis and statistics module, a result presentation module, a production inspection module, a report query module, a production plan update module, and a production plan viewing module. This patent realizes the flexible, dynamic, rapid reorganization and reuse of production processes and production formulas to meet the requirements of efficient production, precise control, and stable product quality.

[0003] Although the above patent can perform intelligent production scheduling management for multi-formula production, when faced with orders for multi-variety, small-batch industrial anti-corrosion coatings, the system's flexibility and response speed are insufficient, and problems in the production process cannot be discovered and resolved in a timely manner, resulting in delayed production progress and inability to deliver products on time. Summary of the invention

[0004] The purpose of the present invention is to provide an automated industrial anti-corrosion coating intelligent scheduling system, which realizes the full-process automated scheduling from order reception to production task execution through the collaborative work of order analysis, scheduling decisions, task instructions and monitoring management, while supporting real-time feedback and dynamic adjustment to ensure the optimal balance between production efficiency and delivery timeliness, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An automated industrial anti-corrosion coating intelligent production scheduling system, comprising:

[0007] An order parsing unit is used to receive and parse production order information, extract the type, quantity and delivery time of the coating required by the order, sort the order information according to the preset production priority rules, and label the order information based on the sorting results;

[0008] The production scheduling decision unit is used to generate a corresponding production scheduling plan based on the output data of the order parsing unit and the monitoring data of real-time monitoring, and adjust the generated production scheduling plan when encountering abnormal conditions;

[0009] The task instruction unit is used to extract the production tasks of the production scheduling plan and generate corresponding production task instructions. The production task instructions are sent to each production equipment based on the data interaction channel. At the same time, the execution status of the production tasks is tracked in real time, and the scheduling of subsequent production tasks is adjusted according to the execution status.

[0010] The visual management unit is used to provide users with a visual display of real-time production status, production scheduling and execution progress, and supports users to input adjustment requirements.

[0011] Furthermore, the order parsing unit includes:

[0012] The order receiving module is used to obtain the order information from the customer order system in real time and pre-process the order information;

[0013] Demand analysis module, used to extract key demand parameters from pre-processed order information, establish order-demand mapping relationship based on key demand parameters, and determine production process and raw materials;

[0014] A priority sorting module is used to load preset rules from a priority rule library, perform priority calculation on order information based on the weight of each rule, perform priority sorting based on the calculation result, and generate an order information list;

[0015] The order marking module is used to match the corresponding batch production rules according to the order quantity, mark the order information as a small batch or large batch order, calculate the production cycle of the order information according to the length of the delivery time field, and mark the order information that does not meet the preset production cycle as an urgent order.

[0016] Furthermore, the priority sorting module also includes:

[0017] Obtain historical order information and parse it, and score the complexity of each order based on the parsing results;

[0018] Analyze the matching degree between the delivery time of historical order information and the actual production schedule, and identify the key factors affecting production efficiency and customer satisfaction;

[0019] Based on the analysis and identification results of historical order information, the weight parameters of the preset rules are adjusted, and the weights of the priority calculation rules are optimized based on the adjusted weight parameters;

[0020] At the same time, real-time monitoring data is obtained, the changing parameters of production conditions are determined based on the feedback information, the priority scores are adjusted, and the priority sequence is updated in real time.

[0021] Furthermore, the order information list includes: order number, demand parameters, estimated production time, resource requirements, priority information and potential risks; wherein, a priority score for each order information is obtained based on the calculation result, and the order information is sorted according to the priority score. If the priority scores are the same, secondary sorting is performed in the order of receipt time of the order information.

[0022] Furthermore, the production scheduling decision unit includes:

[0023] The data integration module is used to integrate the demand parameters, resource requirements and potential risks in the order information list, and combine them with real-time monitoring data to generate a comprehensive data set;

[0024] The production scheduling algorithm module is used to generate a production scheduling plan based on the comprehensive data set and priority sorting results, and optimize the production tasks in the production plan according to the constraints;

[0025] The plan output module is used to convert the optimized production scheduling plan into standardized task instructions, generate the corresponding task list, and send the standardized task instructions to the task instruction unit.

[0026] Furthermore, the production scheduling decision unit also includes:

[0027] Quality control module for:

[0028] Deploy automated testing equipment to collect key quality parameters in the coating production process in real time;

[0029] Real-time monitoring of quality fluctuations in the coating production process based on key quality parameters. When it is detected that the key quality parameters deviate from the preset range, the quality alarm mechanism is triggered, a real-time alarm is issued, and the type of quality problem and its impact range are indicated;

[0030] Link the quality monitoring data of the production process with the traceability information to generate a multi-dimensional quality report.

[0031] Furthermore, the production scheduling algorithm module specifically includes:

[0032] Acquire the order information to be scheduled based on the comprehensive data set, read the order information, and determine the scheduling requirements of the order information, wherein the order information is at least one, and the scheduling requirements include the type, quantity, delivery time and resource requirements of the order;

[0033] Analyze the production scheduling requirements, generate a production scheduling requirement plan, determine key tasks based on the production scheduling requirement plan and priority results, and mark special requirements;

[0034] Analyze production constraints, evaluate equipment operating status and availability, obtain personnel scheduling information, integrate raw material inventory and supply chain data, and mark potential production bottlenecks;

[0035] Optimize the preliminary production schedule based on the priority sorting results;

[0036] According to the real-time monitoring data, the optimized preliminary production schedule is adjusted, the execution order and resource allocation are re-planned, and the production schedule is generated.

[0037] Furthermore, the task instruction unit includes:

[0038] The task conversion module is used to extract the production task data in the production scheduling plan and generate a standardized task instruction set according to the task parameters of the production task data;

[0039] A task distribution module is used to sort the task distribution sequence according to the priority information in the task instruction set, and distribute the task instructions to the corresponding production equipment through the production control interface based on the task distribution sequence;

[0040] The execution monitoring module is used to compare the acquired real-time monitoring data with the production tasks in the production scheduling plan, determine whether the production tasks comply with the production scheduling plan, and generate a task execution report.

[0041] Further, the monitoring module is executed, specifically including:

[0042] Monitor the task execution status of production equipment in real time, and obtain equipment operation status, task execution progress and environmental parameters;

[0043] Based on the extracted real-time monitoring data, the actual completion status of the production task is compared with the expected plan in the production scheduling plan. If a deviation is detected, the deviation attribute is recorded, and the deviation is classified and analyzed based on the historical production data and rule base, the cause of the deviation is identified, and a task execution report is generated.

[0044] Furthermore, the execution monitoring module further includes:

[0045] Collect real-time operation data samples of production equipment, extract key performance indicators of the equipment from each data sample, and calculate the operation abnormality coefficient of the equipment;

[0046] Determine whether the operation abnormality coefficient of the device exceeds the preset abnormality threshold. If so, collect multiple frames of operation image data of the device. Otherwise, confirm that the device is operating normally.

[0047] The device status characteristic factors are extracted from each frame of running image data, the abnormal features and normal features in the image are distinguished, and the abnormal status degree of the device is calculated. If the abnormal status degree exceeds the preset warning threshold, the device operation abnormality alarm is triggered, otherwise it is confirmed that there is no abnormality in the device operation.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The order parsing unit can quickly receive and parse order information, generate production scheduling plans based on real-time data and preset rules, and accurately send production task instructions to each production equipment through the task conversion module and task distribution module. At the same time, it tracks the task execution status in real time, promptly discovers and solves problems in the production process, and provides an intuitive production status display, supporting users to quickly understand the production progress and make adjustments, ensuring that the production progress is as planned, reducing delays, and improving the ability to deliver products on time, thereby significantly improving production efficiency and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a module diagram of the automated industrial anti-corrosion coating intelligent production scheduling system of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In order to solve the technical problems that the system is not flexible and responsive enough when facing orders of industrial anti-corrosion coatings with multiple varieties and small batches, problems in the production process cannot be discovered and solved in time, resulting in delayed production progress and failure to deliver products on time, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0053] An automated industrial anti-corrosion coating intelligent production scheduling system, comprising:

[0054] An order parsing unit is used to receive and parse production order information, extract the type, quantity and delivery time of the coating required by the order, sort the order information according to the preset production priority rules, and label the order information based on the sorting results;

[0055] The production scheduling decision unit is used to generate a corresponding production scheduling plan based on the output data of the order parsing unit and the monitoring data of real-time monitoring, and adjust the generated production scheduling plan when encountering abnormal conditions;

[0056] The task instruction unit is used to extract the production tasks of the production scheduling plan and generate corresponding production task instructions. The production task instructions are sent to each production equipment based on the data interaction channel. At the same time, the execution status of the production tasks is tracked in real time, and the scheduling of subsequent production tasks is adjusted according to the execution status, including:

[0057] The task conversion module is used to extract the production task data in the production scheduling plan, including production batches, product types, process steps, equipment requirements and time requirements, and generate a standardized task instruction set based on the task parameters of the production task data so that it can be recognized and executed by various production equipment, covering equipment operation, raw material allocation and process parameter setting;

[0058] A task distribution module is used to sort the task distribution sequence according to the priority information in the task instruction set, and distribute the task instructions to the corresponding production equipment through the production control interface based on the task distribution sequence;

[0059] The execution monitoring module is used to compare the real-time monitoring data obtained with the production tasks in the production scheduling plan to determine whether the production tasks are in line with the production scheduling plan, and generate a task execution report to record the task completion status and progress;

[0060] The visual management unit is used to provide users with a visual display of real-time production status, production scheduling and execution progress, and supports users to input adjustment requirements.

[0061] In this embodiment, the order parsing unit can quickly receive and parse order information, generate a production scheduling plan based on real-time data and preset rules, and accurately send production task instructions to each production equipment through the task conversion module and the task distribution module. At the same time, it tracks the task execution status in real time, promptly discovers and solves problems in the production process, and provides an intuitive production status display, which supports users to quickly understand the production progress and make adjustments, ensuring that the production progress is as planned, reducing delays, and improving the ability to deliver products on time, thereby significantly improving production efficiency and customer satisfaction.

[0062] In this embodiment, the order parsing unit includes:

[0063] The order receiving module is used to obtain order information from the customer order system in real time and pre-process the order information, including data integrity verification, abnormal data processing, standardization processing, etc.

[0064] Demand analysis module, which is used to extract key demand parameters such as coating type, quantity and delivery time from pre-processed order information, and establish order-demand mapping relationship based on key demand parameters to determine production process and raw materials;

[0065] A priority sorting module is used to load preset rules from a priority rule library, perform priority calculation on order information based on the weight of each rule, perform priority sorting based on the calculation result, and generate an order information list;

[0066] The order information list includes: order number, demand parameters, estimated production time, resource requirements, priority information and potential risks; wherein, based on the calculation result, a priority score of each order information is obtained, and the order information is sorted according to the priority score. If the priority scores are the same, the order information is sorted in the order of the receipt time.

[0067] The order marking module is used to match the corresponding batch production rules according to the order quantity, mark the order information as a small batch or large batch order, calculate the production cycle of the order information according to the length of the delivery time field, and mark the order information that does not meet the preset production cycle as an urgent order.

[0068] In this embodiment, by establishing an order-demand mapping relationship, the production process and raw material requirements are clarified, the targeted nature of the production plan is improved, and the orders can be reasonably sorted according to the urgency and resource requirements, which optimizes the allocation of production resources and improves production efficiency. It is possible to quickly identify orders of different types and urgency, flexibly adjust production strategies, ensure priority processing of urgent orders, shorten the production cycle, and improve the on-time delivery rate, ultimately improving customer satisfaction and the company's market competitiveness.

[0069] In this embodiment, the priority sorting module further includes:

[0070] Obtain and parse historical order information, including production recipe quantity, process requirements, batch size, order completion time, customer satisfaction, etc., and score the complexity of each order based on the analysis results, taking into account factors such as production difficulty, technical requirements, and special process requirements;

[0071] Analyze the matching degree between the delivery time of historical order information and the actual production schedule, and identify the key factors affecting production efficiency and customer satisfaction, including equipment performance, raw material supply, production cost, production cycle, product quality, delivery time, price competitiveness, etc.;

[0072] Based on the analysis and identification results of historical order information, adjust the weight parameters of the preset rules, and optimize the weights of the priority calculation rules based on the adjusted weight parameters to improve the accuracy and real-time performance of the sorting;

[0073] At the same time, it obtains real-time monitoring data, such as equipment status, raw material inventory changes, etc., determines the change parameters of production conditions based on feedback information, adjusts priority scores, and updates priority sequences in real time to ensure the effectiveness and flexibility of production scheduling decisions;

[0074] In this embodiment, by analyzing historical data on the basis of traditional priority rules, optimizing priority rule weights, and improving sorting accuracy and real-time performance, priority sorting becomes more intelligent and dynamic, and can better adapt to changes in the production process, thereby improving the overall performance and efficiency of the scheduling system.

[0075] In this embodiment, the production scheduling decision unit includes:

[0076] The data integration module is used to integrate the demand parameters, resource requirements and potential risks in the order information list, and combine it with real-time monitoring data, such as equipment status, raw material inventory, and personnel scheduling information, to generate a comprehensive data set;

[0077] The production scheduling algorithm module is used to generate a production scheduling plan based on the comprehensive data set and priority sorting results, and optimize the production tasks in the production plan according to the constraints, such as mixed integer linear programming and heuristic algorithms. The constraints include: production orders, equipment capacity, personnel scheduling and other information;

[0078] The plan output module is used to convert the optimized production scheduling plan into standardized task instructions, generate a task list including task numbers, production equipment, process steps, resource requirements and execution sequence, and send the standardized task instructions to the task instruction unit;

[0079] Quality control module for:

[0080] Deploy automated testing equipment, including online coating testers, formula component analyzers, and color difference monitoring equipment, to collect key quality parameters in the coating production process in real time, including coating component ratio, viscosity, solid content, drying time, and finished product color deviation;

[0081] Real-time monitoring of quality fluctuations in the coating production process based on key quality parameters. When it is detected that the key quality parameters deviate from the preset range, the quality alarm mechanism is triggered, a real-time alarm is issued, and the type of quality problem and its impact range are prompted, including equipment-level quality anomalies, batch-level quality deviations, and global quality problem warnings;

[0082] Link the quality monitoring data in the production process with traceability information such as production batches, equipment status, and raw material sources to generate multi-dimensional quality reports, including production batch qualification rate, key quality indicator distribution, and exception handling;

[0083] In this embodiment, by comprehensively considering order demand and real-time production status, ensuring the accuracy and adaptability of the production scheduling plan, standardizing the generation and output of task instructions, simplifying the production process, and improving the accuracy and efficiency of production operations, thereby significantly improving production response speed and order delivery capabilities. Automated testing equipment and data analysis technology monitor the quality of the paint production process in real time, provide quality alarm and traceability functions, and promptly discover and solve quality problems. At the same time, quality data is collected and can be used to continuously improve production processes and product quality, which can effectively improve quality control capabilities in the production process, ensure that paint products meet high quality standards, and achieve rapid response to problems and continuous improvement of production processes, thereby enhancing the overall intelligence and reliability of the system.

[0084] In this embodiment, the production scheduling algorithm module specifically includes:

[0085] Acquire the order information to be scheduled based on the comprehensive data set, read the order information, and determine the scheduling requirements of the order information, wherein the order information is at least one, and the scheduling requirements include the type, quantity, delivery time and resource requirements of the order;

[0086] Analyze the production demand and generate a production demand plan, which includes task priority, required equipment and raw material allocation, and personnel scheduling information; determine key tasks based on the production demand plan and priority results, and mark special requirements, such as orders involving special processes or urgent delivery;

[0087] Analyze production constraints, including production order requirements, equipment capabilities, personnel scheduling and raw material supply, evaluate equipment operating status and availability, including equipment performance parameters, current process status and planned maintenance cycle, obtain personnel scheduling information, determine available working hours and process skill matching, mark scheduling conflicts, integrate raw material inventory and supply chain data, evaluate material availability, and mark potential production bottlenecks;

[0088] Optimize the preliminary production scheduling plan based on the priority sorting results to adapt to complex multi-objective production scheduling scenarios and ensure that the plan takes into account both flexibility and production efficiency;

[0089] Based on real-time monitoring data, such as equipment failure, raw material shortage or staff absenteeism, the optimized preliminary production schedule is adjusted, the execution order and resource allocation of the affected tasks are replanned, and the production schedule is generated.

[0090] In this embodiment, by comprehensively considering key factors such as task priority, equipment, raw materials and personnel, the scientificity and practicality of the production schedule are improved, so that the production schedule can maintain production efficiency and flexibility while meeting multiple objectives, and can respond to changes in production in a timely manner. The system's adaptability and anti-interference ability are enhanced, thereby significantly improving the execution efficiency of the production plan.

[0091] In this embodiment, the monitoring module is executed, specifically including:

[0092] Real-time monitoring of the task execution status of production equipment, including equipment operation status, task completion progress and related operation records, and obtaining equipment operation status, task execution progress and environmental parameters;

[0093] Based on the extracted real-time monitoring data, the actual completion status of the production task is compared with the expected plan in the production scheduling plan. If a deviation is detected, the deviation attributes are recorded, including the deviation type, the scope of the deviation impact and the potential cause. The deviation is classified and analyzed based on the historical production data and rule base, the cause of the deviation is identified, and a task execution report is generated;

[0094] Collect real-time operation data samples of production equipment, extract key performance indicators such as the equipment's operating temperature, vibration frequency, operating power, and task completion rate from each data sample, and calculate the equipment's operating abnormality coefficient;

[0095] Determine whether the operation abnormality coefficient of the device exceeds the preset abnormality threshold. If so, collect multiple frames of operation image data of the device. Otherwise, confirm that the device is operating normally.

[0096] The device status characteristic factors are extracted from each frame of running image data, the abnormal features and normal features in the image are distinguished, and the abnormal status degree of the device is calculated. If the abnormal status degree exceeds the preset warning threshold, the device operation abnormality alarm is triggered, otherwise it is confirmed that there is no abnormality in the device operation.

[0097] In this embodiment, by comparing with the expected plan, deviations in production can be discovered in a timely manner, which helps to respond to and solve problems quickly. It can track equipment status and task execution progress in real time, improve the supervision capabilities of the production site, enhance the accuracy of anomaly detection, ensure timely shutdown and maintenance of equipment under abnormal circumstances, improve the stability and reliability of the production process, reduce production risks, and ensure product quality.

[0098] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An automated industrial anti-corrosion coating intelligent production scheduling system, characterized in that: include: An order parsing unit is used to receive and parse production order information, extract the type, quantity and delivery time of the coating required by the order, sort the order information according to the preset production priority rules, and label the order information based on the sorting results; The production scheduling decision unit is used to generate a corresponding production scheduling plan based on the output data of the order parsing unit and the monitoring data of real-time monitoring, and adjust the generated production scheduling plan when encountering abnormal conditions; The task instruction unit is used to extract the production tasks of the production scheduling plan and generate corresponding production task instructions. The production task instructions are sent to each production equipment based on the data interaction channel. At the same time, the execution status of the production tasks is tracked in real time, and the scheduling of subsequent production tasks is adjusted according to the execution status. The visual management unit is used to provide users with a visual display of real-time production status, production scheduling and execution progress, and supports users to input adjustment requirements.

2. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 1, characterized in that: Order parsing unit, including: The order receiving module is used to obtain the order information from the customer order system in real time and pre-process the order information; Demand analysis module, used to extract key demand parameters from pre-processed order information, establish order-demand mapping relationship based on key demand parameters, and determine production process and raw materials; A priority sorting module is used to load preset rules from a priority rule library, perform priority calculation on order information based on the weight of each rule, perform priority sorting based on the calculation result, and generate an order information list; The order marking module is used to match the corresponding batch production rules according to the order quantity, mark the order information as a small batch or large batch order, calculate the production cycle of the order information according to the length of the delivery time field, and mark the order information that does not meet the preset production cycle as an urgent order.

3. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 2, characterized in that: Prioritization module, also includes: Obtain historical order information and parse it, and score the complexity of each order based on the parsing results; Analyze the matching degree between the delivery time of historical order information and the actual production schedule, and identify the key factors affecting production efficiency and customer satisfaction; Based on the analysis and identification results of historical order information, the weight parameters of the preset rules are adjusted, and the weights of the priority calculation rules are optimized based on the adjusted weight parameters; At the same time, real-time monitoring data is obtained, the changing parameters of production conditions are determined based on the feedback information, the priority scores are adjusted, and the priority sequence is updated in real time.

4. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 3, characterized in that: The order information list includes: order number, demand parameters, estimated production time, resource requirements, priority information and potential risks; wherein, a priority score for each order information is obtained based on the calculation result, and the order information is sorted according to the priority score. If the priority scores are the same, secondary sorting is performed in the order of the receipt time of the order information.

5. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 4, characterized in that: The production scheduling decision unit includes: The data integration module is used to integrate the demand parameters, resource requirements and potential risks in the order information list, and combine them with real-time monitoring data to generate a comprehensive data set; The production scheduling algorithm module is used to generate a production scheduling plan based on the comprehensive data set and priority sorting results, and optimize the production tasks in the production plan according to the constraints; The plan output module is used to convert the optimized production scheduling plan into standardized task instructions, generate the corresponding task list, and send the standardized task instructions to the task instruction unit.

6. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 5, characterized in that: The production scheduling decision unit also includes: Quality control module for: Deploy automated testing equipment to collect key quality parameters in the coating production process in real time; Real-time monitoring of quality fluctuations in the coating production process based on key quality parameters. When it is detected that the key quality parameters deviate from the preset range, the quality alarm mechanism is triggered, a real-time alarm is issued, and the type of quality problem and its impact range are indicated; Link the quality monitoring data of the production process with the traceability information to generate a multi-dimensional quality report.

7. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 6, characterized in that: The production scheduling algorithm module includes: Acquire the order information to be scheduled based on the comprehensive data set, read the order information, and determine the scheduling requirements of the order information, wherein the order information is at least one, and the scheduling requirements include the type, quantity, delivery time and resource requirements of the order; Analyze the production scheduling requirements, generate a production scheduling requirement plan, determine key tasks based on the production scheduling requirement plan and priority results, and mark special requirements; Analyze production constraints, evaluate equipment operating status and availability, obtain personnel scheduling information, integrate raw material inventory and supply chain data, and mark potential production bottlenecks; Optimize the preliminary production schedule based on the priority sorting results; According to the real-time monitoring data, the optimized preliminary production schedule is adjusted, the execution order and resource allocation are re-planned, and the production schedule is generated.

8. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 7, characterized in that: Mission instruction unit, including: The task conversion module is used to extract the production task data in the production scheduling plan and generate a standardized task instruction set according to the task parameters of the production task data; A task distribution module is used to sort the task distribution sequence according to the priority information in the task instruction set, and distribute the task instructions to the corresponding production equipment through the production control interface based on the task distribution sequence; The execution monitoring module is used to compare the acquired real-time monitoring data with the production tasks in the production scheduling plan, determine whether the production tasks comply with the production scheduling plan, and generate a task execution report.

9. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 8, characterized in that: Execution monitoring module, including: Monitor the task execution status of production equipment in real time, and obtain equipment operation status, task execution progress and environmental parameters; Based on the extracted real-time monitoring data, the actual completion status of the production task is compared with the expected plan in the production scheduling plan. If a deviation is detected, the deviation attribute is recorded, and the deviation is classified and analyzed based on the historical production data and rule base, the cause of the deviation is identified, and a task execution report is generated.

10. The automated industrial anti-corrosion coating intelligent production scheduling system according to claim 9, characterized in that: The execution monitoring module also includes: Collect real-time operation data samples of production equipment, extract key performance indicators of the equipment from each data sample, and calculate the operation abnormality coefficient of the equipment; Determine whether the operation abnormality coefficient of the device exceeds the preset abnormality threshold. If so, collect multiple frames of operation image data of the device. Otherwise, confirm that the device is operating normally. The device status characteristic factors are extracted from each frame of running image data, the abnormal features and normal features in the image are distinguished, and the abnormal status degree of the device is calculated. If the abnormal status degree exceeds the preset warning threshold, the device operation abnormality alarm is triggered, otherwise it is confirmed that there is no abnormality in the device operation.

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